PROJECT SUMMARY Methamphetamine (METH) is commonly abused and is a risk factor for HIV. METH also increases risk for adverse outcomes during HIV, including HIV-associated neurocognitive disorder (HAND). Multiple studies have demonstrated the neurotoxic effects of METH and HIV on brain structure and function, as well as neurobehavioral and functional performance. Biomarkers of central nervous system (CNS) injury and resilience would be valuable tools for understanding the METH- and HIV-associated pathogenesis and could provide valuable insights for drug development. The effects of METH and HIV outside the CNS (e.g., vascular and metabolic disease) are also important to consider and are a key gap in the field. Another key gap in the field is the translation of research findings to the clinic, including biomarkers that may be used to detect METH- and HIV-associated CNS injury and functional impairment across disease stages. Even less is known about biomarkers of resilience of the host defense mechanisms. Thus, in response to RFA-DA-18-023, this project proposes to screen a comprehensive panel of clinical and research biomarkers reflective of pathology and resilience with the goal of identifying and validating biosignatures that may improve the clinical assessment and diagnosis of brain and peripheral complications associated with METH and HIV. To accomplish this, we propose to leverage NIDA?s prior investment in UCSD?s NIDA-funded Translational Methamphetamine AIDS Research Center (TMARC), which performed standardized neurobehavioral, neuroimaging, and neuromedical assessments of participants who differed by METH use and HIV infection. We will recall and comprehensively re-assess 200 of these participants. Neuroimaging methods will include measures of cerebral blood flow, CNS metabolite levels, and a novel neuroimaging measure to estimate integrity of the blood-brain barrier (BBB). We will measure a comprehensive biomarker panel in blood, cerebrospinal fluid (CSF), and stool samples, in specimens from their prior baseline visit, which are stored, and from their new visit and then analyze the data using traditional statistical approaches as well as novel techniques rooted in machine-learning and causal inference modeling. This approach will provide unique longitudinal data that will allow for the identification of biosignatures that predict changes in CNS injury and resilience. We will validate the observed biosignatures in an independent cohort of 100 participants who have been previously assessed at UCSD?s HIV Neurobehavioral Research Program and who have comprehensive data and stored specimens (e.g., CSF) available. To better respond to the clinical translation objective of the RFA, we will also compare measured biomarkers to data that are obtained during routine clinic assessments with the goal of identifying a clinical biosignature of METH- and HIV-related CNS injury. A thorough understanding of the impact of METH and HIV on systemic and CNS processes will address key gaps in the field. This insight should also inform the development of assays to inform diagnosis and effective disease and treatment monitoring.